add ability to append template variables to existing ds

Author: shunt16

obsarray/templater/template_util.py

@@ -11,15 +11,19 @@
 
 
 def create_ds(
-    template: Dict[str, Dict], size: Dict[str, int], metadata: Optional[Dict] = None,
-        propagate_ds: Optional[xarray.Dataset] = None,
+    template: Dict[str, Dict],
+    size: Dict[str, int],
+    metadata: Optional[Dict] = None,
+    append_ds: Optional[xarray.Dataset] = None,
+    propagate_ds: Optional[xarray.Dataset] = None,
 ) -> xarray.Dataset:
     """
     Returns template dataset
 
     :param template: dictionary defining ds variable structure, as defined below.
     :param size: dictionary of dataset dimensions, entry per dataset dimension with value of size as int
     :param metadata: dictionary of dataset metadata
+    :param append_ds: base dataset to append with template variables
     :param propagate_ds: template dataset is populated with data from propagate_ds for their variables with
     common names and dimensions. Useful for transferring common data between datasets at different processing levels
     (e.g. times, etc.).
@@ -38,7 +42,7 @@ def create_ds(
     """
 
     # Create dataset
-    ds = xarray.Dataset()
+    ds = append_ds if append_ds is not None else xarray.Dataset()
 
     # Add variables
     ds = TemplateUtil.add_variables(ds, template, size)
@@ -85,7 +89,9 @@ def add_variables(
 
         :returns: dataset with defined variables
         """
+
         for var_name in template.keys():
+
             var = TemplateUtil._create_var(var_name, template[var_name], size)
 
             ds[var_name] = var
@@ -123,7 +129,7 @@ def _create_var(
             )
 
         # Create variable and add to dataset
-        if isinstance(dtype,str):
+        if dtype == str:
             if dtype == "flag":
                 flag_meanings = attributes.pop("flag_meanings")
                 variable = du.create_flags_variable(
@@ -218,21 +224,32 @@ def propagate_values(target_ds, source_ds, exclude=None):
 
         # Find variable names common to target_ds and source_ds, excluding specified exclude variables
         common_variable_names = list(set(target_ds).intersection(source_ds))
-        #common_variable_names = list(set(target_ds.variables).intersection(source_ds.variables))
-        #print(common_variable_names)
+        # common_variable_names = list(set(target_ds.variables).intersection(source_ds.variables))
+        # print(common_variable_names)
 
         if exclude is not None:
-            common_variable_names = [name for name in common_variable_names if name not in exclude]
+            common_variable_names = [
+                name for name in common_variable_names if name not in exclude
+            ]
 
         # Remove any common variables that have different dimensions in target_ds and source_ds
-        common_variable_names = [name for name in common_variable_names if target_ds[name].dims == source_ds[name].dims]
+        common_variable_names = [
+            name
+            for name in common_variable_names
+            if target_ds[name].dims == source_ds[name].dims
+        ]
 
         # Propagate data
         for common_variable_name in common_variable_names:
-            if target_ds[common_variable_name].shape == source_ds[common_variable_name].shape:
-                target_ds[common_variable_name].values = source_ds[common_variable_name].values
-
-        #to do - add method to propagate common unpopulated metadata
+            if (
+                target_ds[common_variable_name].shape
+                == source_ds[common_variable_name].shape
+            ):
+                target_ds[common_variable_name].values = source_ds[
+                    common_variable_name
+                ].values
+
+        # to do - add method to propagate common unpopulated metadata
 
 
 if __name__ == "__main__":